Analysis of influencing factors of carbon emissions in resource-based cities in the Yellow River basin under carbon neutrality target.

The key to coping with climate change is to control carbon emissions from energy consumption. Scientific prediction of energy consumption carbon emissions based on influencing factors is of great significance to the determination of carbon control aim and emission reduction strategies. Given the lack of previous studies on county-level carbon emissions, this paper proposed a systematic approach to study the influencing factors of county-level energy consumption carbon emissions and to predict future emissions. Firstly, the annual energy consumption carbon emissions were calculated based on the method proposed by the Intergovernmental Panel on Climate Change (IPCC). Then the expanded Kaya equation and existing research were combined to select influencing factors for the establishment of the optimal Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, which was used to quantitatively analyze the influencing factors of carbon emissions from energy consumption at the county level. Finally, the emission reduction aims and low-carbon strategies were determined based on scenario analysis. The method was applied to Changxing, a typical county with large energy consumption and carbon emissions. Based on 16 years of data, the STIRPAT carbon emission prediction model was established and the forecast results of future emissions under three different scenarios were obtained. The results indicated that population size, industrial structure, and affluence degree were the three most influential factors, and the influence degree of each factor was quantified to support targeted low-carbon strategies for county-level cities.

This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

Research on peak prediction of urban differentiated carbon emissions -- a case study of Shandong Province, China

Evolutionary characteristics and influencing factors of carbon emissions in China: An examination of a 43-year urban scale

Global increasing carbon emissions have triggered a series of environmental problems and greatly affected the production and living of human beings. This study estimated carbon emissions from land use change in the Beijing-Tianjin-Hebei region during 1990–2020 with the carbon emission model and explored major influencing factors of carbon emissions with the Logarithmic Mean Divisia Index (LMDI) model. The results suggested that the cropland decreased most significantly, while the built-up area increased significantly due to accelerated urbanization. The total carbon emissions in the study area increased remarkably from 112.86 million tons in 1990 to 525.30 million tons in 2020, and the built-up area was the main carbon source, of which the carbon emissions increased by 370.37%. Forest land accounted for 83.58–89.56% of the total carbon absorption but still failed to offset the carbon emission of the built-up area. Carbon emissions were influenced by various factors, and the results of this study suggested that the gross domestic product (GDP) per capita contributed most to the increase of carbon emissions in the study area, resulting in a cumulative increase of carbon emissions by 9.48 million tons, followed by the land use structure, carbon emission intensity per unit of land, and population size. By contrast, the land use intensity per unit of GDP had a restraining effect on carbon emissions, making the cumulative carbon emissions decrease by 103.26 million tons. This study accurately revealed the variation of net carbon emissions from land use change and the effects of influencing factors of carbon emissions from land use change in the Beijing-Tianjin-Hebei region, which can provide a firm scientific basis for improving the regional land use planning and for promoting the low-carbon economic development of the Beijing-Tianjin-Hebei region.

Analysis on the influencing factors of carbon emission in China's logistics industry based on LMDI method

Due to increased global carbon dioxide emissions, the greenhouse effect is being aggravated, which has attracted wide attention. China is committed to promoting the low-carbon development of all industries. This paper analyzed the influencing factors of carbon emissions in the Chinese logistics industry, so as to identify the key factors that influence carbon emissions. Based on the carbon emission data of China’s logistics industry in 2000–2019, this paper applied the carbon emission coefficients issued by the Intergovernmental Panel on Climate Change. For the first time, the Generalized Divisia Index Method was used to analyze the degree of influence of the factors on carbon emissions. This method considered more variables and their relationships. The results showed that (1) the carbon emissions of the logistics industry were increased by 3.22 times from 2000 to 2018, and showed negative growth for the first time in 2019; (2) the added value of the logistics industry is the most important factor in increasing carbon emissions (with a contribution ratio of 65.45%), energy consumption and practical population size are the main factors in carbon emissions. The promotion of this industry is subjected to decreased per capita carbon emissions, which have a large impact on total carbon emissions; (3) the intensity of carbon output is the most important factor in the reduction of carbon emissions (with a contribution ratio of −29.1%), where the energy carbon intensity and per capita added value are the main influencing factors with regard to the reduction of carbon emissions, while energy intensity has a negative inhibitory effect on carbon emissions, and (4) the influencing factors have negative effects on the cumulative inhibition of carbon emissions in the logistics industry, to an extent that is far less than the integral promotion of carbon emissions. Finally, according to the research conclusions of this paper, it is feasible to make recommendations for the carbon reduction of the logistics industry.

Based on the time series decomposition of the Log-Mean Divisia Index, this paper analyzes the driving factors of carbon emissions from energy consumption by introducing the indicators of energy trade in China during the period of 2000–2014. The carbon emissions are decomposed into carbon emission coefficient, population, economic output, energy intensity, energy trade, energy structure and industrial structure effect in the manuscript. The result indicates that economic activity has the largest positive effect on the variation of carbon emissions. The energy trade has a greatest opposite effect on carbon emission change. At the same time, China has achieved a considerable decrease in carbon emission mainly due to the improvement of energy intensity and the optimization of energy and industrial structure. However, the influences of those changes in energy intensity, energy and industrial structure are relatively small. In addition, through the analysis by using a suitable index of energy trade, it was found that improving the conditions of energy trade can effectively optimize the energy structure and reduce the carbon emission in China.

In this paper, we study the radial neural network algorithm for low‐carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. After an in‐depth analysis of the particle swarm algorithm, an improved particle swarm algorithm is proposed to improve the search accuracy and capability of the algorithm by nonlinearly adjusting the inertia weights and introducing the average extreme value factor, in response to the problems of premature convergence and poor search capability that appear in the particle swarm algorithm. Through the analysis and evaluation of the interaction between industrial ecosystem and carbon emission, the main influencing factors of carbon emission are identified, and the size and magnitude of the influence of economic growth, industrial structure, energy intensity, and energy structure on carbon emission are determined; the current situation of the industrial ecological structure is evaluated, and the direction of optimization and adjustment of industrial economic structure, energy structure, and ecological structure is clarified. We construct a multidimensional multiconstraint multimodel industrial ecological structure optimization prediction model, set the development scenarios of economy and society, and optimize the prediction of low‐carbon industrial ecological structure in forest areas; based on the simulation analysis of the prediction results, we propose the direction of industrial ecological structure adjustment and the path of industrial ecological system construction.

The Beijing–Tianjin–Hebei region is an important economic growth pole in China and achieving carbon emission reduction in the region is of great practical significance. Studying the heterogeneity of the influencing factors of carbon emission in this region contributes to formulating targeted regional carbon emission reduction policies. Therefore, this paper adopted thirteen cities as individuals of cross-section and conducted spatial and temporal heterogeneity analysis of the influencing factors of converted carbon emissions in the region with panel data from 2013 to 2018 based on the PGTWR model. From a space-time perspective, the regression coefficient of each influencing factor in this region has obvious heterogeneity, which is mainly reflected in the time dimension. In the study period, the impact of industrial structure, the level of urbanization, energy intensity, and the level of economic growth on carbon emission showed a decline curve, while the impact of the level of opening up and the size of population was on the rise, indicating that more attention should be paid to the latter two factors for the time to come. In terms of space, the differences in the influence of industrial structure and energy intensity on carbon emission vary significantly.

Under the "dual carbon" goal， promoting energy conservation and emission reduction is the key to high-quality economic development. Through innovative analysis， we aim to analyze and predict the influencing factors of carbon emissions in Jiangsu Province from multiple dimensions and provide targeted strategies to reduce carbon emissions. Based on the STRIPAT extended model and LMDI model， we construct an index system of influencing factors of carbon emissions in Jiangsu Province and analyze the impact of different indicators on carbon emissions from multiple dimensions. Using ridge regression and factor analysis methods， we obtain the correlation and contribution rate between carbon emissions and various indicators and predict the carbon emissions in Jiangsu Province using the BP neural network algorithm. The results showed that the ranking of the influencing factors of carbon emissions in Jiangsu Province was： energy consumption， GDP， population， proportion of added value of the tertiary industry， energy consumption structure， proportion of added value of the secondary industry， and proportion of added value of the primary industry. Among them， the proportion of added value of the primary industry and the proportion of added value of the secondary industry had a restraining effect on the growth of carbon emissions， while the remaining factors had a promoting effect. At the same time， according to the prediction results， Jiangsu Province should adjust its industrial and energy structure between 2025 and 2035， increasing the proportion of non-fossil energy to 30%， reducing unit CO2 emissions by 28.6%， and achieving carbon peak. Around 2050， increasing the proportion of non-fossil energy to 50% and reducing unit energy consumption by 46.1% will lead to a rapid decline in CO2 emissions. Eventually， around 2060， the proportion of non-fossil energy will exceed 80%， unit energy consumption will decrease by 54.6%， and CO2 emissions will decrease by 77.9%， achieving carbon neutrality.

Achieving carbon peak before 2030 and carbon neutrality before 2060 is a solemn commitment made by China to address global climate change, and is also one of the main goals for economic and social development in the 14th Five Year Plan and the 2035 vision period. The changes in carbon emissions are directly related to the progress of China's "carbon peak" and "carbon neutrality" goals. Therefore, in-depth research on the influencing factors of carbon emissions has become a key link in promoting the achievement of this goal. In the existing research on carbon emission influencing factors, countries mainly focus on macro scale low-carbon urban carbon emission influencing factors and micro scale low-carbon building full life cycle carbon emission influencing factors. However, there is relatively little research on the influencing factors of carbon emissions in low-carbon communities of urban micro units, and there is still considerable research space. This study conducted an in-depth analysis of the influencing factors of carbon emissions in low-carbon communities using complex network methods. By constructing a complex network model of factors affecting carbon emissions, we identified key nodes and pathways, and explored their interrelationships. The results indicate that factors such as energy structure, resident behavior, building design, and policy implementation play an important role in carbon emissions in low-carbon communities.

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

With accelerating urbanization, building sector has been becoming more important source of China’s total carbon emission. In this paper, we try to calculate the life-cycle carbon emission, analyze influencing factors of carbon emission, and assess the delinking index of carbon emission in China’s building sector. The results show: (i) Total carbon emission in China’s building industry increase from 984.69 million tons of CO2 in 2005 to 3753.98 million tons of CO2 in 2013. The average annual growth rate is 18.21% per year. Indirect carbon emission from building material consumption accounted to 96–99% of total carbon emission. (ii) The indirect emission intensity effect was leading contributor to change of carbon emission. The following was economic output effects, which always contributed to increase in carbon emission. Energy intensity effect and energy structure effect took negligible role to offset carbon emission. (iii) Delinking index show the status between carbon emission and economic output in China’s building industry during 2005–2006 and 2007–2008 was weak decoupling; during 2006–2007 and during 2008–2010 was expansive decoupling; and during 2010–2013 was expansive negative decoupling.

As an important ecological civilization pilot zone and free trade port in China， Hainan Province undertakes the important task of coordinated development of carbon reduction and economic development under the background of the implementation of the strategy of "carbon peak and carbon neutrality." Based on the calculation of carbon source， carbon sink， and net carbon emissions in Hainan Province from 2004 to 2023， the LMDI model and Lasso analysis were used to decompose and screen the influencing factors of carbon emissions in Hainan Province， and four Lasso-Transformer neural network models were included to predict carbon emissions in Hainan Province from 2024 to 2030. The results showed that： ① The trend of total carbon sink in Hainan Province from 2004 to 2023 was relatively stable， and the change trend of net carbon emission was basically consistent with the total carbon source. ② The main influencing factors of carbon emissions in Hainan Province were energy intensity， land carbon emission intensity， economic efficiency， land use structure， population size， and land use efficiency. ③ Through model optimization， the Lasso-PatchTST model was used to predict the carbon emission of Hainan Province from 2024 to 2030 and its influencing factors， and the carbon emission in 2030 was predicted to be 43，455，300 tons. The growth rate of land use efficiency factor was the fastest， and the growth rate of population size was the slowest. By optimizing industrial structure， improving resource utilization efficiency and strengthening ecosystem protection， it can promote the coordinated development of carbon reduction and economy in Hainan Province. The results of this study can provide a reference for decision-making of low-carbon economic development in Hainan Province.